Cardiovascular disease (CVD) is a complex process involving genetic, epigenetic and life history variations in diet, environment and health behaviors. We have assembled an integrated team of physician scientists, experimental cardiologists, physiological biochemists and computational biologists to address this problem. We will apply a new high-resolution metabolic platform to extensively phenotyped subclinical and clinically diagnosed CVD cohorts to discover new metabolic risk factors. The metabolomics platform measures 7000 chemicals in human plasma, including chemicals in most known biochemical pathways. The ongoing predictive health cohort has been extensively phenotyped for subclinical vascular disease (endothelial dysfunction, carotid intima-media thickness, arterial stiffness, and microvascular dysfunction) and a wide variety of biomarkers of oxidative stress and inflammation. The ongoing CVD cohort has clinical CVD (measured as presence/absence of coronary artery disease at angiography and its severity, and previous history of myocardial infarction), and will be followed clinically throughout this project. Bioinformatic tools will be ued to develop functional metabolic and genetic maps of the metabolic risk factors. These functional maps will guide mechanistic studies in mouse models of CVD (partial carotid ligation and apoE-/-) employing novel chemical and protein delivery tools to test the metabolites and perturbed metabolic pathways in CVD development. The discovered metabolic, genetic and pathway profiles will be used with existing knowledge to develop a new vascular disease risk model, which will be validated in independent cohorts with data on 4-year progression of subclinical vascular disease, patients with CVD developing adverse cardiovascular events, and patients with peripheral artery disease being followed for adverse CVD events. The interacting components will create a system to classify CVD risk phenotype in terms of metabolic patterns and predictive models that integrate the metabolic patterns, pathways, and functional networks. The long-term goal is an affordable approach that can be used for predicting disease susceptibility, diagnosis, risk stratification, response to therapy and prognosis.